CN110245842A - A kind of production line Risk Scheduling method of equipment oriented burst major break down - Google Patents
A kind of production line Risk Scheduling method of equipment oriented burst major break down Download PDFInfo
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Abstract
The present invention relates to production line scheduling fields, more particularly, to a kind of production line Risk Scheduling method of equipment oriented burst major break down.The present invention by considered in the evaluation process of apparatus for production line failure risk apparatus for production line happen suddenly major break down influence, by introduce happen suddenly major break down health status, enable use the more acurrate assessment apparatus for production line risk of risk assessment scheme；By establishing apparatus for production line health status model, apparatus for production line burst major break down state weight shared by the entire production line equipment health status is specified, linear weighted function comprehensive assessment apparatus for production line risk is passed through；Improved difference local algorithm increases the diversity of individual while keeping original searching method advantage, and search is effectively prevent to fall into locally optimal solution.Present invention major break down bring risk it can be considered that apparatus for production line happens suddenly, realizes the multiple target Risk Scheduling of production line.
Description
Technical field
The present invention relates to production line scheduling fields, more particularly, to a kind of production line risk of equipment oriented burst major break down
Dispatching method.
Background technique
Due to the complexity of modern manufacturing system electromechanics apparatus for production line, material and apparatus for production line in production process in addition
Component it is uncertain failure etc. many uncertainties, reality production in can occur at random apparatus for production line burst major break down.This
Although kind of apparatus for production line burst major break down probability of happening is low, it is unplanned once to can bring about serious apparatus for production line
Shutdown or great product accident, to reduce the execution of the normal production plan of production capacity, very disruptive, delay in delivery phase.
The Chinese patent publication No. CN104635772A of Nanjing Information engineering Univ, date of publication are on May 20th, 2015, hair
Bright entitled " a kind of adaptive dynamic dispatching method of manufacture system ", although this method can be adaptive in face of the realization of dynamic production environment
Dynamic dispatching is answered, but fails to comprehensively consider the probability and severity that apparatus for production line burst major break down occurs, it is difficult to reaction system
The actual motion risk level of system is made, from the global visual angle of safety, economy and risk can not make overall plans and coordinate and provide rule
The scheduling strategy of wind sheltering danger.
Summary of the invention
It is that a kind of probability of happening is low but influence big extreme risk and its and have for apparatus for production line burst major break down
Cumulative bad feature, to overcome availability risk appraisal procedure to be difficult to the shortcomings that reacting the actual motion risk level of apparatus for production line,
The present invention provides a kind of production line Risk Scheduling methods of equipment oriented burst major break down.
A kind of production line Risk Scheduling method of equipment oriented burst major break down, method includes the following steps:
1) the apparatus for production line health state evaluation model based on hidden Markov model is established；The apparatus for production line is strong
Health state includes health, inferior health, normal, exception, conventional fault, burst major break down state；
2) apparatus for production line is obtained according to the apparatus for production line health state evaluation model of step 1 and is in burst major break down shape
Probability of state；
3) risk of measurement apparatus for production line conventional fault risk and the major break down that happens suddenly, the production line obtained according to step 2
Equipment is in burst major break down state probability, is based on linear weighted function comprehensive assessment apparatus for production line failure risk；
4) it is analyzed around the apparatus for production line risk and its economy both sides relation of step 3 acquisition, constructs one kind
Multiple target risk dispatching model；
5) differential evolution method and the non-dominant genetic algorithm of multiple target, the multiple target risk tune that Optimization Steps 4 are established are combined
Model is spent, the final scheduling scheme of production line is obtained.
The step 1) specifically:
1.1) initialization of model parameter is carried out using segmentation K-means method, it is poly- by continuous iterative model
Class center obtains hidden Markov model initiation parameter λ, i.e. λ=(π, A, B).
π indicates that initial state distribution vector, A indicate that state transition probability distribution matrix, B indicate observed value probability square in formula
Battle array.
1.2) apparatus for production line performance parameter tracking data, training pattern parameter are inputted, until making the probability of observation sequence O
Value it is maximum, and until model parameter restrains, model at this timeFor required hidden Markov chain.
The step 2) specifically:
Using Forward-Backward algorithm, apparatus for production line is calculated in t moment and is in state θiProbability:
In formulaIt is { o to give λ in the observation sequence that t moment exports1,…,ot, state θiProbability,
It is { o to give λ in the observation sequence that t moment exportst+1,…,oT, state θjProbability,It is corresponding for given λ
The maximum probability of observation sequence O.
It calculates apparatus for production line and is in burst major break down state θkProbability pk:
The step 3) specifically:
According to the apparatus for production line burst major break down state probability p calculated in step 2)k, linear group is determined using comentropy
The apparatus for production line burst major break down weight factor δ of conjunction:
H in formulakFor the corresponding comentropy of apparatus for production line burst major break down state, HiFor each health status of apparatus for production line
Corresponding comentropy, m are the number of apparatus for production line health status.
Based on apparatus for production line burst major break down weight factor δ, apparatus for production line failure risk c is assessed.
C=δ CVaRc+(1-δ)Bi
CVaR in formulacFor apparatus for production line burst major break down risk, BiFor apparatus for production line conventional fault risk.
Step 4) the building multiple target risk dispatching model is considered as a whole in terms of economy and risk two, with work
Position balancing the load and apparatus for production line failure risk are optimization aim, construct a kind of multiple target risk dispatching model:
Min f (x)={ Cost, Risk }
4.1) overload time for each balanced bottleneck station and free time, balance station load will be used as risk tune
The economic index of degree, is specifically defined are as follows:
In formula: idtijFor standby time of the vehicle in jth station of i-th sequence, ovtijFor i-th sequence vehicle in jth station
Surcharge preloading duration.
4.2) scheduling model risk indicator is calculated according to step 3):
RISK=δ CVaRc+(1-δ)Bi
The risk dispatching model constraint condition includes: process sequence constraint, machine constraint and continuity constraint.
1) process constrains: it is required that there is successive constraint between the process of same workpiece, i.e., the jth procedure of workpiece i must be the
(j-1) it can just be carried out after procedure:
B in formulaijmIndicate process RijBeginning process time on m platform machine, Sijm=Si(j-1)m=1.
2) machine constrains: same machine can only process a procedure in synchronization, i.e., to process RijIn moment t, t >
0, ifThen Sxym=1 must be invalid (j ≠ y when i=x).
3) continuity constraint: process RijIt cannot interrupt in process:
C in formulaijmIndicate process RijCompletion date.
As a kind of further prioritization scheme of production line Risk Scheduling method of present invention equipment oriented burst major break down,
The non-dominant genetic algorithm of the multiple target of the step 5 the following steps are included:
1. one father population P of random initializtion0, it includes individuals, carry out quickly non-dominant row to the individual in population
Sequence obtains population Pt, and each individual is classified according to non-dominant grade；
2. to population PtBinary championship obtains population Pt', individual p is randomly choosed, it is general with difference using differential variation model
Rate PdIt makes a variation；
3. the variation individual of step 2., which is carried out binomial with other individuals, intersects generation individual q '；
4. to individual q ' with mutation probability PmNew variation individual q " is generated, and q ', q " are merged into progeny population Qt；
5. merging PtAnd QtGenerate combination population Rt；
6. to RtQuick non-dominated ranking is carried out, R is comparedtCrowding distance, and using elitism strategy select RtIn it is N number of
Individual forms population P of new generationt+1；
7. judging whether to meet termination condition, if meeting condition, circulation terminates, and exports result；Otherwise, go to step 2. after
It is continuous to execute.
As a kind of further prioritization scheme of production line Risk Scheduling method of present invention equipment oriented burst major break down,
Differential variation model in the step 5 is as follows:
X in formulabestIndicate that optimum individual in current population, F are zoom factors,It is in population PtWith
Machine selection.
Technical solution of the present invention has following effect:
1) present invention in the evaluation process of apparatus for production line failure risk by considering apparatus for production line burst die
The influence of barrier enables the more acurrate assessment of the risk assessment scheme used to produce by introducing the major break down health status that happens suddenly
Line equipment Risk.
2) present invention specifies apparatus for production line burst major break down state and exists by establishing apparatus for production line health status model
Weight shared by the entire production line equipment health status, by linear weighted function comprehensive assessment apparatus for production line risk, to solve to give birth to
The problem that producing line equipment burst major break down probability of happening is low but harm is serious.
3) present invention design improves the non-dominant genetic algorithm of multiple target and carries out solving risk dispatching model uncertain factor
Caused risk cost.The improved difference local algorithm that the present invention designs is keeping original searching method simple with process
Easily realize, the advantages that algorithm parameter is few, fast convergence rate while, the diversity of individual can be increased, search is effectively prevent to fall into
Enter locally optimal solution.
In conclusion a kind of production line Risk Scheduling method of equipment oriented burst major break down provided by the invention, it can
Consider apparatus for production line burst major break down bring risk, realizes the multiple target Risk Scheduling of production line.
Detailed description of the invention
Fig. 1 is apparatus for production line health status transfer process of the present invention；
Fig. 2 is Risk Scheduling method implementation process of the present invention；
Fig. 3 is the dispatching algorithm flow chart that the present invention combines differential evolution and the non-dominant genetic algorithm of multiple target；
Fig. 4 is the Pareto curve that embodiment DENSGA-II and NSGA-II algorithm obtains.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
The invention discloses a kind of production line Risk Scheduling methods of equipment oriented burst major break down, as shown in Figure 1, production
Line equipment health status includes health, inferior health, normal, exception, conventional fault, burst major break down state.
Step 1):
1.1) initialization that model parameter is carried out using segmentation K-means method, by continuous iterative model cluster centre,
Obtain hidden Markov model initiation parameter λ, i.e. λ=(π, A, B).
π indicates that initial state distribution vector, A indicate that state transition probability distribution matrix, B indicate observed value probability square in formula
Battle array.
1.2) apparatus for production line performance parameter tracking data, training pattern parameter are inputted, until making the probability of observation sequence O
Value it is maximum, and until model parameter restrains, model at this timeFor required hidden Markov chain.
Step 2):
Using Forward-Backward algorithm, apparatus for production line is calculated in t moment and is in state θiProbability:
In formulaIt is { o to give λ in the observation sequence that t moment exports1,…,ot, state θiProbability,
It is { o to give λ in the observation sequence that t moment exportst+1,…,oT, state θjProbability,It is corresponding for given λ
The maximum probability of observation sequence O.
It calculates apparatus for production line and is in burst major break down state θkProbability pk:
Step 3):
According to 2) the middle apparatus for production line burst major break down state probability p calculatedk, linear combination is determined using comentropy
Apparatus for production line burst major break down weight factor δ:
H in formulakFor the corresponding comentropy of apparatus for production line burst major break down state, HiFor each health status of apparatus for production line
Corresponding comentropy, m are the number of apparatus for production line health status.
Based on apparatus for production line burst major break down weight factor δ, assessment sets failure risk c.
C=δ CVaRc+(1-δ)Bi
CVaR in formulacFor apparatus for production line burst major break down risk, BiFor apparatus for production line conventional fault risk.
Step 4):
Building multiple target risk dispatching model is considered as a whole in terms of economy and risk two, with station balancing the load and
Apparatus for production line failure risk is optimization aim, constructs a kind of multiple target risk dispatching model:
Min f (x)={ Cost, Risk }
4.1) overload time for each balanced bottleneck station and free time, balance station load will be used as risk tune
The economic index of degree, is specifically defined are as follows:
In formula: idtijFor standby time of the vehicle in jth station of i-th sequence, ovtijFor i-th sequence vehicle in jth station
Surcharge preloading duration.
4.2) scheduling model risk indicator is calculated according to step 3):
RISK=δ CVaRc+(1-δ)Bi
The risk dispatching model constraint condition includes: process sequence constraint, machine constraint and continuity constraint.
Process constraint: it is required that there is successive constraint between the process of same workpiece, i.e. the jth procedure of workpiece i must be in (j-
1) it can just be carried out after procedure:
B in formulaijmIndicate process RijBeginning process time on m platform machine, Sijm=Si(j-1)m=1.
Machine constraint: same machine can only process a procedure in synchronization, i.e., to process RijIn moment t, t > 0,
IfThen Sxym=1 must be invalid (j ≠ y when i=x).
Continuity constraint: process RijIt cannot interrupt in process:
C in formulaijmIndicate process RijCompletion date.
As shown in figure 3, the non-dominant genetic algorithm of the multiple target of the step 5 the following steps are included:
1. one father population P of random initializtion0, it includes individuals, carry out quickly non-dominant row to the individual in population
Sequence obtains population Pt, and each individual is classified according to non-dominant grade；
2. to population PtBinary championship obtains population Pt', individual p is randomly choosed, it is general with difference using differential variation model
Rate PdIt makes a variation；
3. the variation individual of step 2., which is carried out binomial with other individuals, intersects generation individual q '；
4. to individual q ' with mutation probability PmNew variation individual q " is generated, and q ', q " are merged into progeny population Qt；
5. merging PtAnd QtGenerate combination population Rt；
6. to RtQuick non-dominated ranking is carried out, R is comparedtCrowding distance, and using elitism strategy select RtIn it is N number of
Individual forms population P of new generationt+1；
7. judging whether to meet termination condition, if meeting condition, circulation terminates, and exports result；Otherwise, go to step 2. after
It is continuous to execute.
Differential variation model in the step 5 is as follows:
X in formulabestIndicate that optimum individual in current population, F are zoom factors,It is in population PtWith
Machine selection.
In order to verify the effect of the non-dominant genetic algorithm of improved multiple target (DENSGA-II) proposed in this paper, by its with
The non-dominant genetic algorithm of the multiple target of standard (NSGA-II) compares, and adjusts to the welding production batch plan in certain workshop
Degree.Its production plan is as shown in table 1, this 12 kinds of vehicles are as shown in table 2 in the standard time of 10 bottleneck stations, for station
Load balance is studied.
Table 1 produces yarn batches production plan
| Vehicle | A | B | C | D | E | F | G | H | I | J | K | L |
| Quantity | 4 | 3 | 5 | 2 | 3 | 1 | 6 | 3 | 4 | 2 | 6 | 1 |
The station period that 12 kinds of vehicle bottleneck stations of 2 production line of table need
| Vehicle | MB02 | MB05 | MB07 | MB08 | MB10 | MB13 | MB19 | MB20 | MB21 | MB24 |
| A | 74 | 84 | 61 | 74 | 77 | 78 | 72 | 63 | 78 | 75 |
| B | 86 | 78 | 70 | 78 | 81 | 82 | 75 | 68 | 77 | 83 |
| C | 83 | 75 | 74 | 65 | 84 | 66 | 77 | 80 | 84 | 68 |
| D | 78 | 84 | 84 | 76 | 78 | 83 | 74 | 69 | 77 | 76 |
| E | 61 | 83 | 76 | 85 | 84 | 65 | 78 | 74 | 71 | 64 |
| F | 91 | 83 | 61 | 76 | 79 | 78 | 72 | 62 | 83 | 93 |
| G | 68 | 77 | 81 | 69 | 78 | 84 | 66 | 73 | 78 | 66 |
| H | 79 | 68 | 73 | 84 | 75 | 65 | 76 | 79 | 83 | 67 |
| I | 75 | 77 | 64 | 85 | 77 | 82 | 84 | 64 | 83 | 63 |
| J | 72 | 75 | 86 | 88 | 65 | 74 | 85 | 65 | 68 | 76 |
| K | 83 | 75 | 74 | 70 | 86 | 74 | 75 | 64 | 78 | 84 |
| L | 74 | 92 | 86 | 68 | 74 | 72 | 84 | 64 | 78 | 68 |
In terms of algorithm parameter setting, DENSGA-II and NSGA-II use integer coding, the scaling of DENSGA-II because
Son is set as 0.5, and differential probability 0.9, mutation probability 0.25, multinomial index of variability is 20；The crossover probability of NSGA-II
It is 0.9, mutation probability 0.1, being respectively provided with population scale is 100, and achieving population scale is 50, the number of iterations 250.
Experiment parameter based on arrangement above uses DENSGA-II algorithm and NSGA-II algorithm dialogue automobile body welding respectively
Production line carries out Risk Scheduling, runs 10 times, Fig. 4 has recorded the Pareto curve that DENSGA-II and NSGA-II algorithm obtains.
It is analyzed by Fig. 4, in the quantity of Pareto optimal solution and its uniformity of distribution, the result of DENSGA-II algorithm is all better than
NSGA-II algorithm.Therefore, DENSGA-II is better than NSGA-II algorithm on search performance.
It can be seen that method provided by the invention in production line scheduling from above-mentioned verification result, can guarantee that station is negative
When carrying balanced, while reducing apparatus for production line failure risk.
Claims (7)
1. a kind of production line Risk Scheduling method of facing to manufacture line equipment burst major break down, method includes the following steps:
1) the apparatus for production line health state evaluation model based on hidden Markov model is established；The apparatus for production line health shape
State includes health, inferior health, normal, exception, conventional fault, burst major break down state；
2) apparatus for production line is obtained according to the apparatus for production line health state evaluation model of step 1 and is in burst major break down state
Probability；
3) risk of measurement apparatus for production line conventional fault risk and the major break down that happens suddenly, the apparatus for production line obtained according to step 2
In burst major break down state probability, it is based on linear weighted function comprehensive assessment apparatus for production line failure risk；
4) it is analyzed around the apparatus for production line risk and its economy both sides relation of step 3 acquisition, it is flat with station load
Weighing apparatus and apparatus for production line failure risk are optimization aim, construct a kind of multiple target risk dispatching model.
5) differential evolution method and the non-dominant genetic algorithm of multiple target, the multiple target Risk Scheduling mould that Optimization Steps 4 are established are combined
Type obtains the final scheduling scheme of production line.
2. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as described in claim 1, it is characterised in that:
The step 1) specifically:
1) initialization that model parameter is carried out using segmentation K-means method obtains mould by continuous iterative model cluster centre
Type initiation parameter λ, i.e. λ=(π, A, B)；π indicates that initial state distribution vector, A indicate state transition probability moment of distribution in formula
Battle array, B indicate observed value probability matrix；
2) apparatus for production line performance parameter tracking data, training pattern parameter are inputted, until make the maximum probability of observation sequence O,
And until model parameter restrains, model at this timeFor required hidden Markov chain.
3. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as described in claim 1, it is characterised in that:
The step 2) specifically:
Using Forward-Backward algorithm, apparatus for production line is calculated in t moment and is in state θiProbability:
In formulaIt is { o to give λ in the observation sequence that t moment exports1,...,ot, state θiProbability,For to
Determining λ in the observation sequence that t moment exports is { ot+1,...,oT, state θjProbability,To give the corresponding sight of λ
The maximum probability of sequencing column O；
It calculates apparatus for production line and is in burst major break down state θkProbability pk:
4. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as described in claim 1, it is characterised in that:
The step 3) specifically:
According to the apparatus for production line burst major break down state probability p calculated in step 2)k, linear combination is determined using comentropy
Apparatus for production line burst major break down weight factor δ:
H in formulakFor the corresponding comentropy of apparatus for production line burst major break down state, HiIt is corresponding for each health status of apparatus for production line
Comentropy, m be apparatus for production line health status number；
Based on apparatus for production line burst major break down weight factor δ, apparatus for production line failure risk c is assessed；
C=δ CVaRc+(1-δ)Bi
CVaR in formulacFor apparatus for production line burst major break down risk, BiFor apparatus for production line conventional fault risk.
5. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as described in claim 1, it is characterised in that:
Step 4) the building multiple target risk dispatching model is considered as a whole in terms of economy and risk two, with station load
Balance and apparatus for production line failure risk are optimization aim, construct a kind of multiple target risk dispatching model:
Min f (x)={ Cost, Risk }
4.1) overload time for each balanced bottleneck station and free time, balance station load will be as Risk Scheduling
Economic index is specifically defined are as follows:
In formula: idtijFor standby time of the vehicle in jth station of i-th sequence, ovtijFor vehicle the surpassing in jth station of i-th sequence
Carry the time；
4.2) scheduling model risk indicator is calculated according to step 3):
Risk=δ CVaRc+(1-δ)Bi
The risk dispatching model constraint condition includes: process sequence constraint, machine constraint and continuity constraint；
Process constraint: it is required that there is successive constraint between the process of same workpiece, i.e. the jth procedure of workpiece i must be in the road (j-1)
It can just be carried out after process:
B in formulaijmIndicate process RijBeginning process time on m platform machine, Sijm=Si(j-1)m=1；
Machine constraint: same machine can only process a procedure in synchronization, i.e., to process RijIn moment t, if t > 0Then Sxym=1 must be invalid, j ≠ y when i=x；
Continuity constraint: process RijIt cannot interrupt in process:
C in formulaijmIndicate process RijCompletion date.
6. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as described in claim 1, it is characterised in that:
The non-dominant genetic algorithm of the multiple target of the step 5 the following steps are included:
1. one father population P of random initializtion0, it includes individuals, carry out quick non-dominated ranking to the individual in population and obtain
To population Pt, and each individual is classified according to non-dominant grade；
2. to population PtBinary championship obtains population Pt', individual p is randomly choosed, using differential variation model with differential probability Pd
It makes a variation；
3. the variation individual of step 2., which is carried out binomial with other individuals, intersects generation individual q '；
4. to individual q ' with mutation probability PmNew variation individual q " is generated, and q ', q " are merged into progeny population Qt；
5. merging PtAnd QtGenerate combination population Rt；
6. to RtQuick non-dominated ranking is carried out, R is comparedtCrowding distance, and using elitism strategy select RtIn individual
Form population P of new generationt+1；
7. judging whether to meet termination condition, if meeting condition, circulation terminates, and exports result；Otherwise, step is gone to 2. to continue to hold
Row.
7. the production line Risk Scheduling method of facing to manufacture line equipment burst major break down as claimed in claim 6, it is characterised in that:
Differential variation model in the step 5 is as follows:
X in formulabestIndicate that optimum individual in current population, F are zoom factors,It is in population PtRandom choosing
It selects.
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| CN201910437500.4A CN110245842B (en) | 2019-05-24 | 2019-05-24 | Production line risk scheduling method for sudden large faults of equipment |
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| CN110245842B CN110245842B (en) | 2022-11-25 |
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| CN112507479B (en) * | 2020-11-19 | 2022-03-15 | 电子科技大学 | Oil drilling machine health state assessment method based on manifold learning and softmax |
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